Different types of digital radiography, encompassing both storage phosphor-based computed radiography and flat-panel detector-based direct radiography, have been accepted in medical circles as replacements for conventional screen-film radiography and it is acknowledged that they offer improved workflow efficiency and improved capability for overall image quality.
In a digital radiography system, the radiation exposure energy that is captured on radiation sensitive material is converted, pixel by pixel, to electronic digital image data which is then stored in memory circuitry for subsequent processing and display on suitable electronic image display devices. One driving force in the success of digital radiography is the ability to visualize and communicate stored images, via data networks, to one or more remote locations for analysis and diagnosis. This represents a workflow improvement over the handling and processing that is required for screen-film radiography, where exposed film must first be developed and checked, then packaged and delivered to a remote location for diagnosis.
Digital radiography systems have a variable speed and their performance is mostly noise limited. These systems are relatively easier to use, are somewhat more “forgiving” in terms of setup and exposure technique over film systems, and offer some inherent potential for image quality improvement. At the same time, however, the relative ease of operation and reduction of the need for rigorous attention to procedure can have detrimental effects in practice. Ease of operability can tend to make x-ray technologists more relaxed when selecting exposure techniques and in positioning the patient when taking x-ray exams with digital radiography systems. In some cases, this can effect image quality and ultimately impact the radiologists' ability to make proper and timely diagnosis. Thus, the need for Quality Assurance (QA) with digital radiography systems is not reduced. In some cases, the need for QA with the implementation of digital radiography may even be increased somewhat in order to generate x-ray images that have sufficient diagnostic value to the radiologist or clinician.
The quality of a radiographic image can be quantified from a number of aspects. These can include anatomy positioning, exposure coverage, motion, and anatomy contrast-to-noise (CNR) ratio, for example.
Or a radiographic image to be considered diagnostically acceptable, the contrast of the diagnostically relevant anatomical regions over the background noise level must exceed a threshold, so that the radiologist or clinician can overcome the effects of image noise and accurately perceive anatomical details. This suggests that there should be a proportional relationship between CNR and the diagnostic quality of the radiographic image. Thus, an image that exhibits high overall CNR levels is more likely to be acceptable for diagnosis, whereas an image with moderate to low CNR levels may have only borderline clinical value or may even be unacceptable for diagnosis.
The problem of assessing CNR is made more difficult by the relative complexity of various types of radiographic image. Even within any particular radiographic image, CNR can vary depending upon the type of tissue that is imaged in a particular area of the image. CNR is thus a function of both the image exposure level and the anatomical region, and also a function of spatial frequency in the image, where both anatomical features and noise can be distributed differently.
The value of using CNR estimation for image correction and subsequent rendering, such as to enhance or reduce image contrast from raw image data, has been recognized, for example, refer to U.S. Pat. No. 7,321,674 entitled “Method of Normalizing a Digital Signal Representation of an Image” (Vuylsteke '674). Vuylsteke '674 relates to rendering an image based on its overall CNR value, estimated from the histogram that best characterizes the noise image (at the finest scale frequency band) and the histogram that best characterizes image contrast (the fourth finest scale frequency band). In addition, CNR in this method is computed without consideration for how tissue characteristics vary over different areas and at different spatial frequencies. As a result, computed data using techniques such as described Vuylsteke '674 can be misleading, since the diagnostic information of interest may be in a portion of the image having CNR at a different level than the overall CNR of the full image. Further, because this type of method calculates the contrast from a single frequency band (for example, the fourth finest scale frequency band in the Vuylsteke '674 method), it does not capture the broad spatial frequency spectrum of the anatomical regions. An additional problem relates to how this CNR information is used. Once CNR is computed, an image having a poor CNR may still be processed using the same rendering sequence as an image having acceptable CNR, perhaps with different gain or contrast adjustment settings in an attempt to compensate for low CNR. However, if CNR is below a certain minimum level, meaning that noise levels are excessive, no subsequent processing can “rescue” the image content. As a result, using this conventional approach, an image that is of poor quality is simply processed anyway, and sent for radiologist or clinician viewing. This can negatively impact the diagnosis results and merely defers identification and solution of the imaging problem.
Thus, it is seen that there would be benefits to a system that detects image quality problems earlier in the imaging workflow and makes it possible to identify and correct at least some portion of such problems more quickly, allowing the exposures to be re-taken while the patient is still present at the imaging site, for example.